Spatiotemporal dynamics of tuberculosis in Xinjiang, China: unraveling the roles of meteorological conditions and air pollution via hierarchical Bayesian modeling

China ranks third globally in tuberculosis burden, with Xinjiang being one of the most severely affected regions. Evaluating environmental drivers (e.g., meteorological conditions, air quality) is vital for developing localized strategies to reduce tuberculosis prevalence. Age-standardized incidence rates (ASR) and estimated annual percentage changes (EAPC) quantified global trends. Joinpoint regression analyzed temporal trends in China and Xinjiang, while spatial autocorrelation examined regional patterns. A spatiotemporal Bayesian hierarchical model (INLA-SPDE algorithm) assessed environmental impacts on tuberculosis incidence across 14 Xinjiang prefectures (2010-2022). In 2021, India had the highest ASR (214.39/100,000), while China’s ASR was 36.28/100,000. The Philippines showed the steepest ASR increase (EAPC 0.82, 95% CI 0.50-1.14), whereas China’s ASR declined significantly (EAPC −3.78, 95% CI −3.67 to −3.90). Xinjiang demonstrated a steeper annual reduction (27.15%, 2018-2022). Spatial clustering was significant (Moran’s I > 0.5, $P < 0.01$ ), with an 897.57 km correlation distance. Air pollutants (PM2.5, PM10, CO) positively correlated with incidence, while temperature and wind speed were inversely associated. Regional variations in environmental impacts necessitate location-specific tuberculosis prevention strategies. Targeted surveillance and resource allocation in high-burden areas are critical for advancing global tuberculosis elimination.